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Pattern Recognition, Visual

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The -MDA: An Invariant to Shifting, Scaling, and Rotating Variance for 3D Object Recognition Using Diffractive Deep Neural Network.

Sensors (Basel, Switzerland)
The diffractive deep neural network (DNN) can efficiently accomplish 2D object recognition based on rapid optical manipulation. Moreover, the multiple-view DNN array (MDA) possesses the obvious advantage of being able to effectively achieve 3D object...

Understanding Human Object Vision: A Picture Is Worth a Thousand Representations.

Annual review of psychology
Objects are the core meaningful elements in our visual environment. Classic theories of object vision focus upon object recognition and are elegant and simple. Some of their proposals still stand, yet the simplicity is gone. Recent evolutions in beha...

Resolving the neural mechanism of core object recognition in space and time: A computational approach.

Neuroscience research
The underlying mechanism of object recognition- a fundamental brain ability- has been investigated in various studies. However, balancing between the speed and accuracy of recognition is less explored. Most of the computational models of object recog...

Deeper neural network models better reflect how humans cope with contrast variation in object recognition.

Neuroscience research
Visual inputs are far from ideal in everyday situations such as in the fog where the contrasts of input stimuli are low. However, human perception remains relatively robust to contrast variations. To provide insights about the underlying mechanisms o...

Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli.

Scientific reports
Perception of social stimuli (faces and bodies) relies on "holistic" (i.e., global) mechanisms, as supported by picture-plane inversion: perceiving inverted faces/bodies is harder than perceiving their upright counterpart. Albeit neuroimaging evidenc...

Scene context is predictive of unconstrained object similarity judgments.

Cognition
What makes objects alike in the human mind? Computational approaches for characterizing object similarity have largely focused on the visual forms of objects or their linguistic associations. However, intuitive notions of object similarity may depend...

Robustness to Transformations Across Categories: Is Robustness Driven by Invariant Neural Representations?

Neural computation
Deep convolutional neural networks (DCNNs) have demonstrated impressive robustness to recognize objects under transformations (e.g., blur or noise) when these transformations are included in the training set. A hypothesis to explain such robustness i...

How well do rudimentary plasticity rules predict adult visual object learning?

PLoS computational biology
A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains t...

A neurocomputational model of decision and confidence in object recognition task.

Neural networks : the official journal of the International Neural Network Society
How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confid...

Using drawings and deep neural networks to characterize the building blocks of human visual similarity.

Memory & cognition
Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neur...